Modeling, characterization and analysis of biological shapes and forms are important in many computational biology studies. Shape representation challenges span the spectrum from small scales (e.g., microarray imaging and protein structure) to the macro scale (e.g., neuroimaging of human brains). In this paper, we present a new approach to represent and analyze biological shapes using wavelets. We apply the new technique to multi-spectral shape decomposition and study shape variability between populations using brain cortical and subcortical surfaces. The wavelet-space-induced shape representation allows us to study the multi-spectral nature of the shape's geometry, topology and features. Our results are very promising and, comparing to the spherical-wavelets method, our approach is more compact and allows utilization of diverse wavelet bases.